Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    4 Citations (Scopus)

    Abstract

    In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.
    Original languageEnglish
    Title of host publicationInternational conference on Neural Information Processing (ICONIP 2012)
    Subtitle of host publicationLecture Notes in Computer Science
    EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
    Place of PublicationGermany
    PublisherSpringer
    Pages465-472
    Number of pages8
    Volume7667
    ISBN (Electronic)9783642345005
    ISBN (Print)9783642344992
    DOIs
    Publication statusPublished - 2012
    Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
    Duration: 12 Nov 201215 Nov 2012

    Conference

    Conference19th International Conference on Neural Information Processing 2012
    CountryQatar
    CityDoha
    Period12/11/1215/11/12

    Fingerprint

    Magnetic resonance
    Brain
    Neural networks
    Learning systems
    Feature extraction
    Classifiers
    Neuroimaging
    Research laboratories
    Wavelet transforms
    Testing

    Cite this

    Singh, L., Chetty, G., & Sharma, D. (2012). Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7667, pp. 465-472). Germany: Springer. https://doi.org/10.1007/978-3-642-34500-5_55
    Singh, Lavneet ; Chetty, Girija ; Sharma, Dharmendra. / Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. pp. 465-472
    @inproceedings{e4855ab98efc4904830598a960105b73,
    title = "Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images",
    abstract = "In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.",
    keywords = "Deep Machine Learning, Extreme Machine Learning, MRI, PCA",
    author = "Lavneet Singh and Girija Chetty and Dharmendra Sharma",
    year = "2012",
    doi = "10.1007/978-3-642-34500-5_55",
    language = "English",
    isbn = "9783642344992",
    volume = "7667",
    pages = "465--472",
    editor = "Tingwen Huang and Zhigang Zeng and Chuandong Li and Leung, {Chi Sing}",
    booktitle = "International conference on Neural Information Processing (ICONIP 2012)",
    publisher = "Springer",
    address = "Netherlands",

    }

    Singh, L, Chetty, G & Sharma, D 2012, Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. in T Huang, Z Zeng, C Li & CS Leung (eds), International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7667, Springer, Germany, pp. 465-472, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34500-5_55

    Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. / Singh, Lavneet; Chetty, Girija; Sharma, Dharmendra.

    International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. p. 465-472.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images

    AU - Singh, Lavneet

    AU - Chetty, Girija

    AU - Sharma, Dharmendra

    PY - 2012

    Y1 - 2012

    N2 - In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.

    AB - In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.

    KW - Deep Machine Learning

    KW - Extreme Machine Learning

    KW - MRI

    KW - PCA

    UR - https://link.springer.com/chapter/10.1007%2F978-3-642-34500-5_55

    U2 - 10.1007/978-3-642-34500-5_55

    DO - 10.1007/978-3-642-34500-5_55

    M3 - Conference contribution

    SN - 9783642344992

    VL - 7667

    SP - 465

    EP - 472

    BT - International conference on Neural Information Processing (ICONIP 2012)

    A2 - Huang, Tingwen

    A2 - Zeng, Zhigang

    A2 - Li, Chuandong

    A2 - Leung, Chi Sing

    PB - Springer

    CY - Germany

    ER -

    Singh L, Chetty G, Sharma D. Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. In Huang T, Zeng Z, Li C, Leung CS, editors, International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7667. Germany: Springer. 2012. p. 465-472 https://doi.org/10.1007/978-3-642-34500-5_55